Method for analyzing probe yield sensitivities to IC design

• US 6,210,983 B1
• Filed: 06/15/1999
• Issued: 04/03/2001
• Est. Priority Date: 10/21/1998
• Status: Expired due to Term

• Current Assignee: Texas Instruments, Inc.
• Original Assignee: Texas Instruments, Inc.
• Inventors: Ross, Ron, Atchison, Nick
• Primary Examiner(s): Bowers, Charles
• Assistant Examiner(s): Sarkar, Asok K.
• Application Number:
  US09/333,787
• Time in Patent Office: 658 Days
• Field of Search: 438/14, 438/16, 700/110, 700/121, 702/81, 702/84, 702/179, 702/180, 716/4, 716/19
• US Class Current: 438/14
• CPC Class Codes: G01R 31/2831 Testing of materials or semi-finished products, e.g. semiconductor wafers or substrates G01R31/318511 takes precedence; testing during manufacture H01L22/00
• CPC Class Codes: G01R 31/2846 using hard- or software simulation or using knowledge-based systems, e.g. expert systems, artificial intelligence or interactive algorithms
• CPC Class Codes: H01L 22/20 Sequence of activities consisting of a plurality of measurements, corrections, marking or sorting steps

Abstract

A method for predicting yield limits of semiconductor wafers in a factory, including the steps of generating a wafer probe test pareto, determining a histogram of the distribution from a selected group from the wafer probe test pareto, extracting parametric data from a database from the histogram, screening the parametric data for values of the parametric data outside of a predetermined range, determining if an average value of the screened parametric data shows a sensitivity to variations in the parametric data, determining specification limits of the screened parametric data, and using the specification limits to form an operating window to show the sensitivity.

Claims

1. A method for predicting yield limits of semiconductor wafers in a factory, comprising the steps of:

generating a wafer probe test pareto;

determininig a histogram of the distribution from a selected group from the wafer probe test pareto;

extracting parametric data from a database from said histogram;

screening the parametric data for values of the parametric data outside of a predetermined range;

determining if an average value of said screened parametric data shows a sensitivity to variations in said parametric data;

determining specification limits of said screened parametric data; and

using said specification limits to form an operating window to show said sensitivity.